Determinants of Maritime Piracy

I Introduction

Maritime piracy has been in the media, especially with attention directed to the Horn of Africa. ATALANTA, the European Union’s naval mission, patrolled along the coast of Somalia to combat maritime piracy and protect trading vessels against attacks. As it seems, fading media attention does not presuppose a reduced danger of maritime piracy globally.

Global shipping routes are highly important for trade. Piracy attacks are a potential threat for crew and cargo on the ship. The cost intensive deployment of international naval forces in Somalia shows how serious countries take the threat whose trade is affected. Interestingly not all piracy attacks are successful, and the ratio varies from country to country and over time. So what drives piracy attacks, why and when are they successful?

II Research Design

The idea for this paper is to research a model which can explain piracy under which circumstances attacks are successful.

Does the number of attacks decrease the likelihood of attacks being successful?

The dependent variable is the success rate of piracy attacks, calculated by the number of successful attacks divided by the total number of attacks. We expect that mainly the total number of attacks has an impact on this ratio. The fact that the dependent variable actually consists of our key independent variable is dangerous. However, we think that there must be a visible learning effect, either from law enforcement bodies, the shipping crew, or the pirates. So far this was the only feasible way we could have a look at this effect.

Furthermore, additional exogenous variables will be included that in theory should have an impact on the inspected success rate. GDP (per capita per year) as a mirror for the economic incentives to conduct piracy is expected to influence the success rate over time. Likewise, a country’s ratio of coast line to its land area should be a good demarker, whether piracy attacks happen more often.

1. After a certain amount of piracy attacks, the success rate has a peek and will decrease.
2. The higher the GDP, the less incentive there is to conduct an attack and only less skilled pirates will make attempts, hence the success rate decreases.
3. The higher the coast-land ratio is, the more people decide to conduct attacks and skilled pirates emerge, hence the success rate increases.

III Preparation of Dataset

Principal Gathering

Since 1992, the International Maritime Bureau (IMB) has collected all reported piracy attack globally. Since then it publishes annually an overview of all attacks that happened in a year. These annual reports provided by the IMB contain detailed information about every incident, which allows for further analysis of distinct types of piracy attacks, for instance successful attacks v. attempted attacks.

The annual reports were scraped with text analysis tools. Our team received a “ready to use” dataset from a research project from the university of Tennessee, including all global piracy attacks from 1994 to 2014.

Additional Data Gathering: World Bank and Wikipedia

The original dataset contains the attacks that were reported by the victims of piracy. As an additional variable, relevant to our field of piracy investigation and patrols, we were intrigued by the relative effect that a longer coast length of a country has on the level of attacks that country suffers. To address this question we parsed a table titled “List of countries by length of coastline” from a Wikipedia page that had, in turn, used information from the CIA World Factbook. We then merged this coastline data with our existing dataframe using a ‘right outer join’.

Of critical interest to us were the respective ‘Coast/Area’ ratios (measured in km of coast length to km of square land) that serves as an insightful control for our country dependent variable.

The information about the gross domestic product comes from the worldbank and was scraped with the WDI package for R. The scraped data comes in a country-year format, thus it comes already in a format we need to conduct our analysis.

Cleaning

Firstly, we reduced the key dataset on piracy attacks to the 8 countries with the highest number of attacks. The remaining countries are:

1. Bangladesh
2. Brazil
3. India
4. Indonesia
5. Malaysia
6. Nigeria
7. Philippines
8. Vietnam

We also consider high levels of activity in the Gulf of Aden and piracy around Somalia.

Secondly, the all data needed to be merged. After the additionally gathered data was clean, we merged into the original dataset on piracy the variables for GDP and the coastline ratio. The initial plan was to reshape this dataset into panel data. However, as you will see in our R-code the reshape was not fully succsessful. We did not get rid of all first-level variables, or in other words we were unable to aggregate to the desired country_year level. We deleted all irrelevant variables, renamed the remaining ones with more intuitive names.

In the next section we explain, why we decided to continue with a new dataset produced with excel.

A Work Around with Excel

A major challenge in the preparation of our data was the change of our units of observations for our dataset MaritimePiracyTennessee.csv from ‘incidents of pirate attack’ to the intended unit of ‘country-year’ for our prepared ‘shippingraw.csv’ dataset.

After several attempts to remove duplicates and particular values in the transformation process we determined to save our intermediate progress. We then proceeded to open and transform the dataset using Microsoft Excel, using the find and replace function supported in this suite. This excursion with another suite was intentionally limited to just these two steps, with data preparation continuing in Analysis.R.

The offending code:

Step 1: Attempt to drop rows conditional given particular values using R

SuccRatCtryYr <- table(shipping\(year, shipping\)closest_coastal_state, shipping$Incident_type_recode==1) SuccRatCtryYr class(SuccRatCtryYr)

Step 2: Attempt to remove duplicate rows using R

duplicated(total4) newtotal4 <- total4[duplicated(total4)==‘FALSE’, ]

IV Visualizing and Examining the Data

## [1] "NULL" "NULL" "NULL"

##                  V1                           V2
## 1                 #                           km
## 2     World[Note 2]                            <U+0097>
## 3     other[Note 3]                            <U+0097>
## 4            Canada 7000100000000000000<U+2660>1
## 5         Indonesia 7000200000000000000<U+2660>2
## 6 Greenland[Note 4]                            <U+0097>
##                                     V3
## 1                                    #
## 2 7006116230600000000<U+2660>1,162,306
## 3   7005356000000000000<U+2660>356,000
## 4   7005202080000000000<U+2660>202,080
## 5    7004547160000000000<U+2660>54,716
## 6    7004440870000000000<U+2660>44,087

## [1] "V1" "V7"

## [1] "V1" "V7"

3D Globes using the ‘threejs’ Package

Many thanks to Bryan W. Lewis and his website for the creation and help in using the threejs package. The 3D globes are full navigable, with pinch-zooming enabled for trackpads to help with examining statistical results in particular regions.

Global Overview: Piracy and Maritime Defence Propensity

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Nb. Country borders highlighted green; Indonesia highlighted red; Arc length indicates vessel status (moving to stationary)

H1: Stationary ships are more likely to be attacked than moving ships in Southeast Asia. H0: Stationary ships are no less, or less likely, to be attacked than moving ships in Southeast Asia.

In this globe we take a regional focus for Southeast Asia. We can observe that whether a ship was moving (dot) or stationary (arc) appears to make little difference to the number of relative pirate attacks. This could be potentially due to the archipelago maritime geography; with many ports concentrated in a small area pirates may have similar accessibility to pirating trade both for anchored ships as well as for ships underway.

Regional Focus: Piracy in the Gulf of Aden & Arabian Sea

Nb. Country borders highlighted green; Somalia highlighted red; Arc length indicates vessel status (stationary to moving)

H1: Stationary ships are more likely to be attacked than moving ships in the Arabian Sea. H0: Stationary ships are no less, or less likely, to be attacked than moving ships in the Arabian Sea.

In this globe we take a regional focus for the Arabian Sea that includes the Gulf of Aden. We can observe that whether a ship was moving (dot) or stationary (arc) appears to make a large difference to the number of relative pirate attacks. Ships that were underway were more likely to be attacked. This is likely due to how large oceangoing ship traffic often transits through the Arabian Sea, but does not anchor. Pirates are therefore forced to conduct mobile pirate raids.